research-article

Improved Tourism Recommendation System

Authors:

Duanyu FengAuthors Info & Claims

ICMLC '20: Proceedings of the 2020 12th International Conference on Machine Learning and Computing

Pages 121 - 126

https://doi.org/10.1145/3383972.3384074

Published: 26 May 2020 Publication History

Abstract

The recommendation system is one of popular machine learning algorithms and has been widely used in areas, especially e-commerce platforms. There are many tourist cities in China, and many tourists are entangled in what places to visit. Inspired by this, in order to improve the satisfaction of tourism users and increase the profitability of tourism companies or platforms, this paper establishes a tourism recommendation system to understand the travel preferences of different users and recommend for them a tourist city worth visiting. This paper first collects user's travel preference data through questionnaires, including the features' scores and comprehensive scores of indicators of the cities that have been visited, and then analyzes the data by establishing a recommendation system based on collaborative filtering. Specifically, the ridge regression method is first used to calculate the degree of each feature that affects the user's judgment on the tourist city, and uses it as feature weights. Secondly, the feature weights are used to calculate the similarity between the two cities. Finally, according to the weighted similarity and the user's historical scores for some cities, urban heat optimization model is introduced innovatively and the list of recommended cities is output. After the recommendation is completed, relevant data obtained by using the questionnaire is tested, and parameters are adjusted in multiple steps to ensure the high performance of the model. The end result is excellent results.

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Cited By

Karthiyayini JAnandhi R(2024)To Analyze the Various Machine Learning Algorithms That Can Effectively Process Large Volumes of Data and Extract Relevant Information for Personalized Travel RecommendationsSN Computer Science10.1007/s42979-024-02667-x5:4Online publication date: 27-Mar-2024
https://doi.org/10.1007/s42979-024-02667-x
Zacarias HCangondo GSouza-Pereira LGarcia NSilva BPombo N(2023)Application of Content-Base Recommendation Algorithms on Mobile Travel Applications2023 1st International Conference on Advanced Innovations in Smart Cities (ICAISC)10.1109/ICAISC56366.2023.10085680(1-5)Online publication date: 23-Jan-2023
https://doi.org/10.1109/ICAISC56366.2023.10085680
Zhao S(2023)A Novel Personalized Business Recommendation Analysis Method Based on Big Data Intelligence2023 2nd International Conference on Artificial Intelligence and Autonomous Robot Systems (AIARS)10.1109/AIARS59518.2023.00059(257-263)Online publication date: Jul-2023
https://doi.org/10.1109/AIARS59518.2023.00059
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Improved Tourism Recommendation System

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ICMLC '20: Proceedings of the 2020 12th International Conference on Machine Learning and Computing

February 2020

607 pages

ISBN:9781450376426

DOI:10.1145/3383972

Copyright © 2020 ACM.

Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Shenzhen University: Shenzhen University

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Association for Computing Machinery

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Publication History

Published: 26 May 2020

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the National Key R&D Program of China

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ICMLC 2020

ICMLC 2020: 2020 12th International Conference on Machine Learning and Computing

February 15 - 17, 2020

Shenzhen, China

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Cited By

Karthiyayini JAnandhi R(2024)To Analyze the Various Machine Learning Algorithms That Can Effectively Process Large Volumes of Data and Extract Relevant Information for Personalized Travel RecommendationsSN Computer Science10.1007/s42979-024-02667-x5:4Online publication date: 27-Mar-2024
https://doi.org/10.1007/s42979-024-02667-x
Zacarias HCangondo GSouza-Pereira LGarcia NSilva BPombo N(2023)Application of Content-Base Recommendation Algorithms on Mobile Travel Applications2023 1st International Conference on Advanced Innovations in Smart Cities (ICAISC)10.1109/ICAISC56366.2023.10085680(1-5)Online publication date: 23-Jan-2023
https://doi.org/10.1109/ICAISC56366.2023.10085680
Zhao S(2023)A Novel Personalized Business Recommendation Analysis Method Based on Big Data Intelligence2023 2nd International Conference on Artificial Intelligence and Autonomous Robot Systems (AIARS)10.1109/AIARS59518.2023.00059(257-263)Online publication date: Jul-2023
https://doi.org/10.1109/AIARS59518.2023.00059
Theodorakopoulos LHalkiopoulos CPapadopoulos D(2023)Applying Big Data Technologies in Tourism Industry: A Conceptual AnalysisTourism, Travel, and Hospitality in a Smart and Sustainable World10.1007/978-3-031-26829-8_21(337-352)Online publication date: 30-May-2023
https://doi.org/10.1007/978-3-031-26829-8_21
Dai YMeng SLiu QLiu X(2022)Knowledge-aware Graph Attention Network with Distributed & Cross Learning for Collaborative Recommendation2022 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom)10.1109/ISPA-BDCloud-SocialCom-SustainCom57177.2022.00044(294-301)Online publication date: Dec-2022
https://doi.org/10.1109/ISPA-BDCloud-SocialCom-SustainCom57177.2022.00044
Latha R(2022)Enhancing recommendation competence in nearest neighbour modelsPhysica A: Statistical Mechanics and its Applications10.1016/j.physa.2021.126835592(126835)Online publication date: Apr-2022
https://doi.org/10.1016/j.physa.2021.126835
Abbasi-Moud ZHosseinabadi SKelarestaghi MEshghi F(2022)CAFOBExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.116877199:COnline publication date: 1-Aug-2022
https://dl.acm.org/doi/10.1016/j.eswa.2022.116877
Garipelly VAdusumalli PSingh P(2021)Travel Recommendation System Using Content and Collaborative Filtering - A Hybrid Approach2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT51525.2021.9579907(1-4)Online publication date: 6-Jul-2021
https://doi.org/10.1109/ICCCNT51525.2021.9579907
Ogunseyi TBo TYang C(2021)A privacy-preserving framework for cross-domain recommender systemsComputers and Electrical Engineering10.1016/j.compeleceng.2021.10721393:COnline publication date: 1-Jul-2021
https://dl.acm.org/doi/10.1016/j.compeleceng.2021.107213
Afsahhosseini FAl-Mulla Y(2020)Machine Learning in TourismProceedings of the 2020 3rd International Conference on Machine Learning and Machine Intelligence10.1145/3426826.3426837(53-57)Online publication date: 18-Sep-2020
https://dl.acm.org/doi/10.1145/3426826.3426837

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